Automatic fluid segmentation in retinal optical coherence tomography images using attention based deep learning

Optical coherence tomography (OCT) is one of the most commonly used ophthalmic diagnostic tech-niques. Macular Edema (ME) is the swelling of the macular region in the eye. Segmentation of the fluid region in the retinal layer is an important step in detecting lesions. However, manual segmentation is often a time consuming and subjective process. In this paper, an improved U-Net segmentation method is proposed. In this method, the attention mechanism is introduced to automatically locate the fluid region, which avoids the problem of excessive calculation in multi-stage methods. At the same time, the use of dense skip connections which combines high-level and low-level features makes the segmen-tation results more precise. The loss function is a joint loss, including weighted binary cross entropy loss, dice loss, and regression loss, where regression loss is used to avoid the problem of merging multiple fluid regions into one. The experimental results show that the proposed method can adapt to the OCT scans acquired by various imaging scanning devices, and this method is more effective than other start-of -the-art fluid segmentation methods. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

An improved U-Net segmentation method with attention mechanism and dense skip connections is proposed in this paper, which makes the segmentation results more precise and avoids excessive calculation. The method includes a joint loss function to address the problem of merging multiple fluid regions into one.